Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture.
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ABSTRACT:
SUBMITTER: Bravo Sanchez FJ
PROVIDER: S-EPMC8333097 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The chiffchaff complex is a group of common forest bird species, notorious for the number of cryptic taxa recently discovered, being a great example of speciation in action. Vocalizations have been crucial to unveil its hidden diversity. In this study we quantitatively analyze the acoustic characteristics of their calls with permutational analysis of variance, canonical variate analysis and a self-organizing map, to determine their variability and differences. We related these differences with the geographical and genetic distances between taxonomic groups, by means of Pearson correlations. We used recordings from Xeno-canto, an open database of bird vocalizations. Inter-taxa distances based on call traits were broadly consistent with geographic distances but not correlated with genetic distances. The Iberian Chiffchaff (Phylloscopus ibericus), presumably the most ancient lineage, was the most central in the variation space, while the Siberian Chiffchaff (P. collybita tristis) was the most peripheric and also very uniform, in contrast with the Canarian Chiffchaff (P. canariensis) highly variable, as expected by the "character release hypothesis" on islands. Calls proved to be an excellent tool, especially amenable for non-biased mathematical analyses which, combined with the wide availability of records in Xeno-canto, greatly facilitates the widespread use of this methodology in a wide range of species and geographical areas.
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Project description:Adoption of commercial photoreactors as standards for photocatalysis research could be limited by high cost. We report the development of the Wisconsin Photoreactor Platform (WPP), an open-source photoreactor architecture potentially suitable for general adoption. The WPP integrates inexpensive commercial components and common high-intensity LEDs in a 3D-printed enclosure. Dimensions and features of WPP reactors can be readily varied and configurations easily reproduced. WPP performance is evaluated using literature transformations driven by light of disparate wavelengths.
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Project description:BackgroundAtrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations.ObjectiveThe purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone.MethodsPatients presenting for cardioversion of AF (n = 51) were given wrist-worn fitness trackers containing PPG sensors (Jawbone Health). Standard 12-lead electrocardiograms over-read by board-certified cardiac electrophysiologists were used as the reference standard. The accuracy of PPG signals to discriminate AF from sinus rhythm was evaluated by conventional measures of heart rate variability, a long short-term memory (LSTM) neural network given heart rate data only, and a deep convolutional-recurrent neural net (DNN) given the raw PPG data.ResultsFrom among 51 patients with persistent AF (age 63.6 ± 11.3 years; 78% male; 88% white), we randomly assigned 40 to train and 11 to test the algorithms. Whereas logistic regression analysis of heart rate variability yielded an area under the receiver operating characteristic curve (AUC) of 0.717 (sensitivity 0.741; specificity 0.584), the LSTM model given heart rate data exhibited AUC of 0.954 (sensitivity 0.810; specificity 0.921), and the DNN model given raw PPG data yielded the highest AUC of 0.983 (sensitivity 0.985; specificity 0.880).ConclusionA deep learning model given the raw PPG-based signal resulted in AF detection with high accuracy, performing better than conventional analyses relying on heart rate series data alone.
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